SPSS Tutorial for Data Analysis - SPSS for Beginners
Comprehensive SPSS tutorial covering various statistical analyses, from basic tests to advanced regression techniques. Ideal for beginners seeking practical data analysis skills using IBM SPSS Statistics.
Linear Regression and Linear Models
Learn powerful statistical tools: linear regression and models. Predict continuous values using various data types. Covers least squares, multiple regression, t-tests, ANOVA, and R implementation.
CBW's Introduction to R Workshop - June 21-22, 2021
Learn R programming essentials: scripting, statistical tests, loops, functions, linear models, and R Markdown in this comprehensive workshop for beginners.
Earth - Stats and Data Analysis
Comprehensive statistics course covering central tendency, hypothesis testing, regression, and advanced analytical techniques for data analysis in earth sciences.
Introductory Hypothesis Testing
Learn essential concepts and techniques for statistical hypothesis testing, including p-values, errors, and various test types for means and proportions.
Working with StatCrunch
Learn to use StatCrunch for data analysis: importing, visualizing, and analyzing data with various statistical methods. Master creating charts, performing tests, and exporting results efficiently.
Stats Examples
Comprehensive statistical training covering key concepts, probability, distributions, hypothesis testing, and advanced analyses with step-by-step examples and practical applications.
Hypothesis Testing and P-Values
Learn key concepts in statistical inference, including p-values, hypothesis testing, t-tests, and ANOVA. Develop skills to choose appropriate tests and interpret results for data-driven decision making.
Choosing the Test, With Tests
Learn to select and apply statistical tests, interpret p-values, and perform analyses using Excel, covering t-tests, regression, and chi-square tests for various data types.
Hypothesis Testing Statistics - Examples and T-Test Hypothesis Testing
Comprehensive overview of hypothesis testing in statistics, covering key concepts, types of tests, and practical examples. Ideal for learners seeking to understand and apply statistical hypothesis testing methods.
Biostatistics Mini-Lectures
Explore essential biostatistical analysis techniques through concise presentations, covering correlation, t-tests, ANCOVA, MANOVA, non-parametric analysis, and more.
Hypothesis Testing
Learn hypothesis testing fundamentals, including rejection regions, p-values, error types, power calculations, and statistical significance. Explore Z and t tests for one mean, with practical examples and assumptions.
NOC- Probability and Statistics
Comprehensive coverage of probability and statistics concepts, including distributions, hypothesis testing, estimation methods, and descriptive statistics. Ideal for deepening statistical knowledge.
T-Test: Fundamentals and Applications - Lecture 9
Explore the fundamentals and practical applications of t-tests in statistical analysis, enhancing your ability to interpret and conduct hypothesis testing effectively.
Statistical Methods in Hypothesis Testing: Z-Test and T-Test - Part 1
Explore statistical methods for hypothesis testing, focusing on z-test and t-test applications and their significance in data analysis.
Being Intentional: AB Testing and Data Minimization - PEPR '24
Explore data minimization techniques for AB testing, focusing on statistical approaches that don't require linking to individual customer IDs, promoting privacy-conscious analytics practices.
Data Analysis in Python - From Descriptive Statistics to Advanced Analytics
Master data analysis fundamentals in Python, from environment setup and data cleaning to advanced statistical methods like T-tests, ANOVA, and regression, with practical business applications and visualization techniques.
Bivariate Analysis in R
Learn bivariate analysis techniques in R, including t-tests, ANOVA, chi-square, correlations, and linear regression. Gain practical skills for data science and statistics using RStudio.
R Tutorials
Comprehensive R tutorials covering data management, analysis, and visualization, with hands-on exercises using real datasets and practical applications in statistics and data science.
R for Beginners
Comprehensive R tutorials for beginners, covering installation, data manipulation, statistical analysis, and visualization. Hands-on approach with downloadable data files for practical learning.
Power & Sample Size in R
Learn to calculate power and sample size for various statistical tests in R, from t-tests to ANOVA and multivariate analyses, enhancing your ability to design robust studies.